Template-Instance Loss for Offline Handwritten Chinese Character Recognition

Xiao, Yao; Meng, Dan; Lu, Cewu; Tang, Chi-Keung

doi:10.1109/icdar.2019.00058

Cited by 20 publications

(7 citation statements)

References 23 publications

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“…In conclusion, the accuracy comparison of studies within the last 5 years is as follows (Table 4): [20] 97.45 SAE (Chen et al, 2023) [21] 97.19 Hierarchical CNN (Aleskerova et al, 2020) [12] 92.10 HDE (Cao et al, 2020) [13] 97.14 Melnyk-Net (Melnyk et al, 2020) [10] 97.61 [22] 96.74 ACPM (Zu et al, 2022) [23] 97.80 RSST (Yu et al, 2023) [16] 96.05…”

Section: Research On Handwritten Chinese Character Recognition Method...mentioning

confidence: 99%

A Review of the Current Status of AI Research in Handwritten Chinese Character Recognition

Ye-eun

2024

SHS Web of Conferences

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With the continuous development of artificial intelligence technology, there have been attempts to utilize AI in the field of linguistics. In this context, research and development in AI-based Chinese character recognition technology has spanned 40 years, with its outcomes attracting significant attention. However, in the field of Chinese character recognition, the research into offline handwritten recognition technology is particularly challenging due to the unique characteristics of handwritten Chinese characters. The main issue in current research is the significant decrease in accuracy for characters composed of similar components or characters with similar overall shapes. The focus of research solutions has been on modern mechanical programming and other engineering aspects, while research that integrates the inherent characteristics of Chinese characters themselves has been relatively overlooked. The author believes that combining engineering technology research with a deeper understanding of the nature of Chinese characters can solve many of the current problems. This paper reviews the current state of related domestic and international research to identify areas with significant potential for improvement and progress in research, and finally proposes directions for future research.

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Section: Research On Handwritten Chinese Character Recognition Method...mentioning

confidence: 99%

A Review of the Current Status of AI Research in Handwritten Chinese Character Recognition

Ye-eun

2024

SHS Web of Conferences

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“…In particular, the existing Chinese text methods mainly aim at handwriting text recognition [13,14,19,28,76,79] or handwriting character recognition [9,11,77,80,84,94,95,97] thanks to the detailed specifications formulated by the CASIA-HWDB database [39]. For example, Xiao et al [81] put forward a template-instance loss to distinguish the similar Chinese characters pairs in the feature domain. Xiao et al [86] utilized an iterative attention mechanism to progressively concentrate on the distinguishable regions of Chinese characters.…”

Section: Existing Text Recognition Methodsmentioning

confidence: 99%

Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study

Chen¹,

Yu²,

Ma³

et al. 2021

Preprint

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The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly for English texts, whereas ignoring the pivotal role of Chinese texts. As another widely-spoken language, Chinese text recognition in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on Chinese text recognition to the lack of reasonable dataset construction standards, unified evaluation methods, and results of the existing baselines. To fill this gap, we manually collect Chinese text datasets from publicly available competitions, projects, and papers, then divide them into four categories including scene, web, document, and handwriting datasets. Furthermore, we evaluate a series of representative text recognition methods on these datasets with unified evaluation methods to provide experimental results. By analyzing the experimental results, we surprisingly observe that state-of-the-art baselines for recognizing English texts cannot perform well on Chinese scenarios. We consider that there still remain numerous challenges under exploration due to the characteristics of Chinese texts, which are quite different from English texts. The code and datasets are made publicly available at https://github.com/ FudanVI/benchmarking-chinese-text-recognition. Figure 1. Three reasons for the scarce attention of Chinese text recognition. (a) People may use different ways to crop text regions, which leads to unfair comparison. (b) It is necessary to specify the equivalence between lowercase and uppercase, half-width and full-width, simplified and traditional characters. (c) The existing methods are mainly evaluated with English datasets rather than Chinese datasets.

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“…Furthermore, they used the Conditional Random Field (CRF) to achieve global optimization. In addition, Xiao et al [47] proposed two new loss functions to accomplish the HCCR task in 2019. They created the character template to address the inherent similarity between Chinese characters.…”

Section: Improve the Accuracy Of Recognitionmentioning

confidence: 99%

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

et al. 2023

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Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

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Template-Instance Loss for Offline Handwritten Chinese Character Recognition

Cited by 20 publications

References 23 publications

A Review of the Current Status of AI Research in Handwritten Chinese Character Recognition

A Review of the Current Status of AI Research in Handwritten Chinese Character Recognition

Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

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